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Battery formation is important for ensuring the quality and service life of cells in lithium-ion battery (LIB) production. During the formation process, fault cells, such as low open-circuit voltage cells, are screened offline after the charging stage since, in most formation protocols, the online screening process is absent. This can lead to energy waste and extend the rework cycle of the fault cells in the LIB formation process. To address this problem, this paper considers the online fault cell screening problem, the formation pre-screening, in the LIB formation process as a classification task and proposes a data-driven model based on incomplete time-series data for formation pre-screening. First, the proposed model transforms segments of the incomplete charging voltage curve (ICVC) of the LIB as tokens, which is a more compact and less redundant data representation of the ICVC. Then, the attention-based feature encoder, Transformer encoder (TE), captures the dependency between tokens to extract features for the formation pre-screening. Finally, a task-specified decoder, feature enhance decoder (FED), is used to screen out fault cells online. The effectiveness of the proposed model is verified using real-world production data collected from a specific type of 18,650 lithium-ion cell under one formation protocol. The results on the investigated industrial dataset show that the proposed model achieves an accuracy of 98.73% and a miss rate of 1.92% during formation pre-screening, which is a 2.49% improvement in accuracy and an 8.98% decrease in miss rate compared with the deep residual network baseline. These results demonstrate the feasibility of using incomplete formation-stage voltage curves for online fault-cell pre-screening, which has the potential to reduce unnecessary charging and rework time in LIB production.
He et al. (Thu,) studied this question.